Building a Virtual Power Plant.

Executive summary

A profitable energy supplier faces a strategic choice: remain a low-cost retailer or become an energy technology company. This case proposes a city-scale virtual power plant pilot that connects household energy assets, uses AI to optimise supply and demand, and creates a repeatable platform for new services. The recommended approach protects the core business while testing the technology, partnerships, customer proposition and commercial model before scaling.

Business context

GetEnergised is a fictional, mid-sized UK energy company operating a small portfolio of onshore wind farms while also buying power on the wholesale market and reselling it to retail and business customers. Most revenue comes from price-sensitive households attracted by its promise of low cost and strong service.

The company is profitable and has invested in operational efficiency and customer service. However, the market is changing. Higher-income customers increasingly value green energy, active management of consumption, household generation and local energy trading. Net-zero policy, support for virtual power plants and rapid advances in smart devices, IoT and AI are creating an opening for more integrated energy services.

The strategic choice

The recommended direction is to develop a virtual power plant: a digital service connecting customer-owned electric vehicles, batteries, solar panels and smart-home devices. IoT would provide live asset data, while AI would forecast renewable generation and demand, then recommend when connected assets should consume, store or return electricity to the grid.

The initial market should be environmentally conscious, higher-income households willing to participate in flexible energy services. Once the model is proven, the platform could extend to business customers, housing developments and local energy communities.

Three implementation challenges

Technology and operational capability

GetEnergised would need to connect and optimise many different devices reliably, supported by new strengths in data engineering, AI, cybersecurity and real-time operations.

Ecosystem coordination

Delivery depends on technology suppliers, device manufacturers, asset owners, local authorities and regulators. Integration, accountability, data access and commercial ownership must be explicit.

Investment and organisational transition

The programme requires significant investment and shifts the business from low-cost supply towards technology-enabled services. Poorly managed change could create internal resistance or weaken the existing proposition.

Why these challenges matter

The strategy moves the company beyond the capabilities that currently make it successful. Technical failure or poor data quality could affect energy decisions and damage customer trust. Progress depends on partners with different priorities, systems and regulatory duties, while investment must be committed before demand and returns are proven.

Leadership therefore needs to protect the core business while giving the new venture sufficient time, funding and permission to test. Without a clear customer value story, the initiative risks becoming an expensive technology programme rather than a viable new business.

A staged innovation strategy

1. Start with a focused pilot

Launch a funded pilot in one city rather than attempt a company-wide transformation. The pilot should connect a limited range of household assets and test the complete proposition: enrolment, device integration, forecasting, optimisation, customer control, settlement and support.

2. Build from the current position

GetEnergised already generates renewable energy, trades on the wholesale market, supplies retail and business customers and operates customer-service, billing and IT capabilities. Its profitability, customer relationships and consumption data are valuable complementary assets. However, the organisation is designed to sell energy efficiently—not manage thousands of connected assets in real time.

Competitors are already experimenting through cross-sector partnerships, while larger incumbents can invest more heavily. AI will accelerate the market by improving forecasting and real-time optimisation, but it also raises new requirements for privacy, explainability, security and accountability.

3. Add the missing capabilities

The pilot needs a cross-functional team covering energy operations, digital product management, data science, cybersecurity, regulation, customer experience and organisational change. Human accountability and the ability to override automated recommendations must remain clear.

Capability gaps and cost should be managed through partnerships with electric-vehicle, battery and smart-home providers, a local authority and an experienced platform supplier. Government grants should be pursued where appropriate. Regulators should be engaged early, with data standards, commercial rights and partner responsibilities agreed before launch.

4. Make the customer proposition explicit

Customers need a simple promise: lower energy costs, greater use of renewable energy and more control over household consumption. Participation terms and data use must be transparent, and customers should retain meaningful control over their assets. Internally, leaders should show how the new service strengthens—rather than replaces—the company’s existing low-cost, great-service position.

5. Design for repeatable scaling

Scaling should follow evidence, not optimism. The pilot’s technical architecture, operating model, governance and commercial agreements should be designed for reuse across locations. Advantage is unlikely to come from the core technology alone; it will come from customer relationships, consumption insight, access to connected devices, partner agreements and dependable integration.

Value creation and value capture

Value created

Lower bills, increased renewable-energy use, improved grid flexibility and greater customer control.

Value captured

Subscriptions, a share of trading and flexibility income, improved retention and access to higher-value customers.

Measures of success

• Customer adoption and trust: participation, retention, satisfaction, opt-out rates and privacy complaints.
• Operational performance: forecast accuracy, asset availability, response times and service incidents.
• Financial return: revenue per participant, trading and flexibility income, retention benefits, operating cost and payback.
• Environmental and grid impact: renewable energy used, peak demand shifted and carbon avoided.
• Responsible AI and security: model errors, human overrides, cybersecurity incidents and audit findings.
• Scalability: the time and cost required to add customers, devices, locations and partners.

Strategic takeaway

The strongest path is neither a technology-first transformation nor a defensive extension of the existing retail model. It is a staged business-model experiment: prove customer value and operational reliability in one location, build distinctive complementary assets, and scale only when the economics, governance and partnership model are repeatable.